Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations18897
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

households is highly overall correlated with population and 2 other fieldsHigh correlation
latitude is highly overall correlated with longitudeHigh correlation
longitude is highly overall correlated with latitudeHigh correlation
median_house_value is highly overall correlated with median_incomeHigh correlation
median_income is highly overall correlated with median_house_valueHigh correlation
population is highly overall correlated with households and 2 other fieldsHigh correlation
total_bedrooms is highly overall correlated with households and 2 other fieldsHigh correlation
total_rooms is highly overall correlated with households and 2 other fieldsHigh correlation

Reproduction

Analysis started2025-03-03 01:33:32.495059
Analysis finished2025-03-03 01:33:53.771983
Duration21.28 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

longitude
Real number (ℝ)

High correlation 

Distinct838
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.59418
Minimum-124.35
Maximum-114.31
Zeros0
Zeros (%)0.0%
Negative18897
Negative (%)100.0%
Memory size147.8 KiB
2025-03-03T07:03:53.991835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-124.35
5-th percentile-122.47
Q1-121.8
median-118.53
Q3-118.02
95-th percentile-117.08
Maximum-114.31
Range10.04
Interquartile range (IQR)3.78

Descriptive statistics

Standard deviation2.0038145
Coefficient of variation (CV)-0.016755118
Kurtosis-1.3280542
Mean-119.59418
Median Absolute Deviation (MAD)1.31
Skewness-0.28170677
Sum-2259971.1
Variance4.0152725
MonotonicityNot monotonic
2025-03-03T07:03:54.277966image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.31 153
 
0.8%
-118.3 141
 
0.7%
-118.27 136
 
0.7%
-118.32 134
 
0.7%
-118.28 133
 
0.7%
-118.29 131
 
0.7%
-118.36 130
 
0.7%
-118.19 128
 
0.7%
-118.35 127
 
0.7%
-118.14 122
 
0.6%
Other values (828) 17562
92.9%
ValueCountFrequency (%)
-124.35 1
 
< 0.1%
-124.3 2
 
< 0.1%
-124.27 1
 
< 0.1%
-124.26 1
 
< 0.1%
-124.25 1
 
< 0.1%
-124.23 3
< 0.1%
-124.22 1
 
< 0.1%
-124.21 3
< 0.1%
-124.19 4
< 0.1%
-124.18 6
< 0.1%
ValueCountFrequency (%)
-114.31 1
 
< 0.1%
-114.49 1
 
< 0.1%
-114.55 1
 
< 0.1%
-114.56 1
 
< 0.1%
-114.57 3
< 0.1%
-114.58 2
< 0.1%
-114.59 1
 
< 0.1%
-114.6 3
< 0.1%
-114.61 3
< 0.1%
-114.62 1
 
< 0.1%

latitude
Real number (ℝ)

High correlation 

Distinct859
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.66329
Minimum32.54
Maximum41.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2025-03-03T07:03:54.531487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum32.54
5-th percentile32.81
Q133.94
median34.28
Q337.73
95-th percentile39.04
Maximum41.95
Range9.41
Interquartile range (IQR)3.79

Descriptive statistics

Standard deviation2.1497734
Coefficient of variation (CV)0.060279728
Kurtosis-1.1267725
Mean35.66329
Median Absolute Deviation (MAD)1.35
Skewness0.4461914
Sum673929.2
Variance4.6215259
MonotonicityNot monotonic
2025-03-03T07:03:54.821930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.05 214
 
1.1%
34.08 213
 
1.1%
34.06 212
 
1.1%
34.04 195
 
1.0%
34.09 194
 
1.0%
34.07 193
 
1.0%
34.02 189
 
1.0%
34.03 175
 
0.9%
33.93 172
 
0.9%
34.1 169
 
0.9%
Other values (849) 16971
89.8%
ValueCountFrequency (%)
32.54 1
 
< 0.1%
32.55 2
 
< 0.1%
32.56 9
 
< 0.1%
32.57 16
0.1%
32.58 26
0.1%
32.59 11
0.1%
32.6 8
 
< 0.1%
32.61 11
0.1%
32.62 11
0.1%
32.63 18
0.1%
ValueCountFrequency (%)
41.95 2
< 0.1%
41.92 1
 
< 0.1%
41.88 1
 
< 0.1%
41.86 3
< 0.1%
41.84 1
 
< 0.1%
41.82 1
 
< 0.1%
41.81 2
< 0.1%
41.8 3
< 0.1%
41.79 1
 
< 0.1%
41.78 3
< 0.1%

housing_median_age
Real number (ℝ)

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.332698
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2025-03-03T07:03:55.107601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q119
median30
Q338
95-th percentile52
Maximum52
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.390898
Coefficient of variation (CV)0.42242614
Kurtosis-0.7921755
Mean29.332698
Median Absolute Deviation (MAD)9
Skewness0.025443754
Sum554300
Variance153.53436
MonotonicityNot monotonic
2025-03-03T07:03:55.449952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 1214
 
6.4%
36 836
 
4.4%
35 803
 
4.2%
16 702
 
3.7%
34 666
 
3.5%
17 618
 
3.3%
33 591
 
3.1%
26 576
 
3.0%
32 538
 
2.8%
37 517
 
2.7%
Other values (42) 11836
62.6%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 45
 
0.2%
3 38
 
0.2%
4 124
0.7%
5 183
1.0%
6 118
0.6%
7 120
0.6%
8 166
0.9%
9 175
0.9%
10 229
1.2%
ValueCountFrequency (%)
52 1214
6.4%
51 47
 
0.2%
50 130
 
0.7%
49 131
 
0.7%
48 169
 
0.9%
47 193
 
1.0%
46 237
 
1.3%
45 281
 
1.5%
44 344
 
1.8%
43 344
 
1.8%

total_rooms
Real number (ℝ)

High correlation 

Distinct4980
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2265.1829
Minimum2
Maximum8874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2025-03-03T07:03:55.803900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile604.8
Q11408
median2037
Q32898
95-th percentile4658.2
Maximum8874
Range8872
Interquartile range (IQR)1490

Descriptive statistics

Standard deviation1249.5242
Coefficient of variation (CV)0.55162179
Kurtosis1.5942679
Mean2265.1829
Median Absolute Deviation (MAD)719
Skewness1.0658858
Sum42805161
Variance1561310.8
MonotonicityNot monotonic
2025-03-03T07:03:56.096737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1527 18
 
0.1%
1613 17
 
0.1%
1582 16
 
0.1%
2127 16
 
0.1%
1722 15
 
0.1%
1717 15
 
0.1%
1471 15
 
0.1%
1703 15
 
0.1%
1607 15
 
0.1%
2053 14
 
0.1%
Other values (4970) 18741
99.2%
ValueCountFrequency (%)
2 1
 
< 0.1%
6 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
15 2
< 0.1%
16 1
 
< 0.1%
18 4
< 0.1%
19 1
 
< 0.1%
20 2
< 0.1%
21 1
 
< 0.1%
ValueCountFrequency (%)
8874 1
< 0.1%
8806 1
< 0.1%
8803 1
< 0.1%
8259 1
< 0.1%
8254 1
< 0.1%
8206 1
< 0.1%
8146 1
< 0.1%
8072 1
< 0.1%
8020 1
< 0.1%
8005 1
< 0.1%

total_bedrooms
Real number (ℝ)

High correlation 

Distinct1269
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean464.13404
Minimum2
Maximum1444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2025-03-03T07:03:56.408366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile134
Q1290
median419
Q3600
95-th percentile949
Maximum1444
Range1442
Interquartile range (IQR)310

Descriptive statistics

Standard deviation244.20534
Coefficient of variation (CV)0.52615262
Kurtosis0.54127019
Mean464.13404
Median Absolute Deviation (MAD)147
Skewness0.82971245
Sum8770741
Variance59636.25
MonotonicityNot monotonic
2025-03-03T07:03:56.695557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280 53
 
0.3%
345 49
 
0.3%
393 49
 
0.3%
331 49
 
0.3%
343 48
 
0.3%
348 48
 
0.3%
309 47
 
0.2%
394 47
 
0.2%
328 47
 
0.2%
272 47
 
0.2%
Other values (1259) 18413
97.4%
ValueCountFrequency (%)
2 2
 
< 0.1%
3 5
< 0.1%
4 6
< 0.1%
5 4
< 0.1%
6 4
< 0.1%
7 6
< 0.1%
8 7
< 0.1%
9 7
< 0.1%
10 8
< 0.1%
11 9
< 0.1%
ValueCountFrequency (%)
1444 1
< 0.1%
1438 1
< 0.1%
1432 1
< 0.1%
1424 1
< 0.1%
1423 1
< 0.1%
1410 1
< 0.1%
1409 1
< 0.1%
1404 1
< 0.1%
1401 2
< 0.1%
1395 1
< 0.1%

population
Real number (ℝ)

High correlation 

Distinct3051
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1238.5804
Minimum3
Maximum3580
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2025-03-03T07:03:56.978922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile339
Q1770
median1126
Q31599
95-th percentile2563.2
Maximum3580
Range3577
Interquartile range (IQR)829

Descriptive statistics

Standard deviation663.3777
Coefficient of variation (CV)0.5355952
Kurtosis0.65268337
Mean1238.5804
Median Absolute Deviation (MAD)400
Skewness0.85445874
Sum23405453
Variance440069.97
MonotonicityNot monotonic
2025-03-03T07:03:57.263250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
891 25
 
0.1%
1052 24
 
0.1%
1227 24
 
0.1%
761 23
 
0.1%
850 23
 
0.1%
1005 22
 
0.1%
782 22
 
0.1%
872 21
 
0.1%
999 21
 
0.1%
753 21
 
0.1%
Other values (3041) 18671
98.8%
ValueCountFrequency (%)
3 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 4
< 0.1%
9 2
< 0.1%
11 1
 
< 0.1%
13 2
< 0.1%
14 3
< 0.1%
15 2
< 0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
3580 2
< 0.1%
3574 1
< 0.1%
3572 1
< 0.1%
3570 2
< 0.1%
3569 1
< 0.1%
3567 1
< 0.1%
3566 1
< 0.1%
3565 1
< 0.1%
3563 1
< 0.1%
3562 2
< 0.1%

households
Real number (ℝ)

High correlation 

Distinct1137
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean433.16505
Minimum2
Maximum1157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2025-03-03T07:03:57.516282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile123
Q1275
median394
Q3560
95-th percentile876
Maximum1157
Range1155
Interquartile range (IQR)285

Descriptive statistics

Standard deviation224.58173
Coefficient of variation (CV)0.51846688
Kurtosis0.33979972
Mean433.16505
Median Absolute Deviation (MAD)136
Skewness0.74943087
Sum8185520
Variance50436.955
MonotonicityNot monotonic
2025-03-03T07:03:57.777919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306 57
 
0.3%
386 55
 
0.3%
429 54
 
0.3%
335 54
 
0.3%
282 54
 
0.3%
284 51
 
0.3%
297 51
 
0.3%
362 50
 
0.3%
340 50
 
0.3%
330 49
 
0.3%
Other values (1127) 18372
97.2%
ValueCountFrequency (%)
2 3
 
< 0.1%
3 4
< 0.1%
4 3
 
< 0.1%
5 6
< 0.1%
6 4
< 0.1%
7 9
< 0.1%
8 8
< 0.1%
9 8
< 0.1%
10 6
< 0.1%
11 4
< 0.1%
ValueCountFrequency (%)
1157 1
 
< 0.1%
1153 1
 
< 0.1%
1152 1
 
< 0.1%
1151 4
< 0.1%
1150 3
< 0.1%
1149 1
 
< 0.1%
1148 1
 
< 0.1%
1147 3
< 0.1%
1146 2
< 0.1%
1144 2
< 0.1%

median_income
Real number (ℝ)

High correlation 

Distinct11715
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7332881
Minimum0.4999
Maximum9.6062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2025-03-03T07:03:58.021385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.4999
5-th percentile1.57486
Q12.5391
median3.5
Q34.6629
95-th percentile6.81096
Maximum9.6062
Range9.1063
Interquartile range (IQR)2.1238

Descriptive statistics

Standard deviation1.6139922
Coefficient of variation (CV)0.43232456
Kurtosis0.45394414
Mean3.7332881
Median Absolute Deviation (MAD)1.0458
Skewness0.79035605
Sum70547.946
Variance2.6049707
MonotonicityNot monotonic
2025-03-03T07:03:58.286966image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.125 48
 
0.3%
4.125 44
 
0.2%
2.625 44
 
0.2%
2.875 44
 
0.2%
3.875 40
 
0.2%
4 36
 
0.2%
3 36
 
0.2%
3.375 36
 
0.2%
3.625 36
 
0.2%
4.375 33
 
0.2%
Other values (11705) 18500
97.9%
ValueCountFrequency (%)
0.4999 12
0.1%
0.536 10
0.1%
0.5495 1
 
< 0.1%
0.6433 1
 
< 0.1%
0.6825 1
 
< 0.1%
0.6831 1
 
< 0.1%
0.696 1
 
< 0.1%
0.6991 1
 
< 0.1%
0.7007 1
 
< 0.1%
0.7025 1
 
< 0.1%
ValueCountFrequency (%)
9.6062 1
< 0.1%
9.6047 1
< 0.1%
9.6023 1
< 0.1%
9.5908 1
< 0.1%
9.5862 1
< 0.1%
9.5823 1
< 0.1%
9.5561 1
< 0.1%
9.5551 1
< 0.1%
9.532 1
< 0.1%
9.5271 1
< 0.1%

median_house_value
Real number (ℝ)

High correlation 

Distinct3768
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201590.1
Minimum14999
Maximum500001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size147.8 KiB
2025-03-03T07:03:58.565928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum14999
5-th percentile65400
Q1116900
median176300
Q3258300
95-th percentile446200
Maximum500001
Range485002
Interquartile range (IQR)141400

Descriptive statistics

Standard deviation111062.13
Coefficient of variation (CV)0.55093047
Kurtosis0.42947719
Mean201590.1
Median Absolute Deviation (MAD)67100
Skewness0.97918288
Sum3.809448 × 109
Variance1.2334796 × 1010
MonotonicityNot monotonic
2025-03-03T07:03:58.844126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500001 639
 
3.4%
137500 116
 
0.6%
162500 113
 
0.6%
112500 97
 
0.5%
187500 87
 
0.5%
225000 84
 
0.4%
87500 76
 
0.4%
350000 72
 
0.4%
150000 62
 
0.3%
175000 62
 
0.3%
Other values (3758) 17489
92.5%
ValueCountFrequency (%)
14999 4
< 0.1%
17500 1
 
< 0.1%
22500 3
< 0.1%
25000 1
 
< 0.1%
26600 1
 
< 0.1%
26900 1
 
< 0.1%
27500 1
 
< 0.1%
30000 2
< 0.1%
32500 4
< 0.1%
32900 1
 
< 0.1%
ValueCountFrequency (%)
500001 639
3.4%
500000 26
 
0.1%
499100 1
 
< 0.1%
499000 1
 
< 0.1%
498800 1
 
< 0.1%
498700 1
 
< 0.1%
498400 1
 
< 0.1%
497600 1
 
< 0.1%
497400 1
 
< 0.1%
496400 2
 
< 0.1%

ocean_proximity
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size147.8 KiB
<1H OCEAN
8278 
INLAND
6067 
NEAR OCEAN
2444 
NEAR BAY
2103 
ISLAND
 
5

Length

Max length10
Median length9
Mean length8.0540827
Min length6

Characters and Unicode

Total characters152198
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEAR BAY
2nd rowNEAR BAY
3rd rowNEAR BAY
4th rowNEAR BAY
5th rowNEAR BAY

Common Values

ValueCountFrequency (%)
<1H OCEAN 8278
43.8%
INLAND 6067
32.1%
NEAR OCEAN 2444
 
12.9%
NEAR BAY 2103
 
11.1%
ISLAND 5
 
< 0.1%

Length

2025-03-03T07:03:59.130006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-03T07:03:59.396663image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ocean 10722
33.8%
1h 8278
26.1%
inland 6067
19.1%
near 4547
14.3%
bay 2103
 
6.6%
island 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 27408
18.0%
A 23444
15.4%
E 15269
10.0%
12825
8.4%
O 10722
 
7.0%
C 10722
 
7.0%
< 8278
 
5.4%
1 8278
 
5.4%
H 8278
 
5.4%
I 6072
 
4.0%
Other values (6) 20902
13.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 122817
80.7%
Space Separator 12825
 
8.4%
Math Symbol 8278
 
5.4%
Decimal Number 8278
 
5.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 27408
22.3%
A 23444
19.1%
E 15269
12.4%
O 10722
 
8.7%
C 10722
 
8.7%
H 8278
 
6.7%
I 6072
 
4.9%
L 6072
 
4.9%
D 6072
 
4.9%
R 4547
 
3.7%
Other values (3) 4211
 
3.4%
Space Separator
ValueCountFrequency (%)
12825
100.0%
Math Symbol
ValueCountFrequency (%)
< 8278
100.0%
Decimal Number
ValueCountFrequency (%)
1 8278
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 122817
80.7%
Common 29381
 
19.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 27408
22.3%
A 23444
19.1%
E 15269
12.4%
O 10722
 
8.7%
C 10722
 
8.7%
H 8278
 
6.7%
I 6072
 
4.9%
L 6072
 
4.9%
D 6072
 
4.9%
R 4547
 
3.7%
Other values (3) 4211
 
3.4%
Common
ValueCountFrequency (%)
12825
43.7%
< 8278
28.2%
1 8278
28.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 152198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 27408
18.0%
A 23444
15.4%
E 15269
10.0%
12825
8.4%
O 10722
 
7.0%
C 10722
 
7.0%
< 8278
 
5.4%
1 8278
 
5.4%
H 8278
 
5.4%
I 6072
 
4.0%
Other values (6) 20902
13.7%

Interactions

2025-03-03T07:03:51.108460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-03-03T07:03:38.834177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:41.846406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:44.027648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:45.785847image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:47.583436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-03-03T07:03:39.097953image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-03-03T07:03:49.513222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:51.490270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:34.732311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:37.072043image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:39.346950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:42.355675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:44.421406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:46.228862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:47.923119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:49.688569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:51.684538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:34.985531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:37.329461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:39.642901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:42.591512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:44.624303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:46.420884image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:48.097250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:49.855988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:52.091442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:35.221477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:37.573873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:40.431454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:42.807926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:44.822783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:46.611537image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:48.290507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:50.047509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:52.280357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:35.520994image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:37.850777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:40.710150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:43.062727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:45.011462image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:46.814378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:48.490100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:50.241978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-03-03T07:03:35.819687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:38.109883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:41.015077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:43.304734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:45.212082image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:47.036408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-03-03T07:03:52.653999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:36.062259image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:38.356799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:41.290527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:43.535392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:45.387314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:47.228633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:48.872422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:50.735755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:52.843792image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:36.301815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:38.589651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:41.584093image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:43.780005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:45.571060image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:47.403039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:49.062751image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-03T07:03:50.915341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-03-03T07:03:59.609059image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
householdshousing_median_agelatitudelongitudemedian_house_valuemedian_incomeocean_proximitypopulationtotal_bedroomstotal_rooms
households1.000-0.220-0.0670.0430.1230.0360.0640.8870.9720.895
housing_median_age-0.2201.0000.023-0.1420.086-0.1410.196-0.223-0.248-0.303
latitude-0.0670.0231.000-0.880-0.174-0.0920.475-0.125-0.046-0.003
longitude0.043-0.142-0.8801.000-0.059-0.0030.4300.1160.0440.020
median_house_value0.1230.086-0.174-0.0591.0000.6680.303-0.0010.0950.210
median_income0.036-0.141-0.092-0.0030.6681.0000.1290.007-0.0030.288
ocean_proximity0.0640.1960.4750.4300.3030.1291.0000.0770.0470.031
population0.887-0.223-0.1250.116-0.0010.0070.0771.0000.8490.789
total_bedrooms0.972-0.248-0.0460.0440.095-0.0030.0470.8491.0000.904
total_rooms0.895-0.303-0.0030.0200.2100.2880.0310.7890.9041.000

Missing values

2025-03-03T07:03:53.093744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-03T07:03:53.487505image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY
1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY
2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY
3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY
4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY
5-122.2537.8552.0919.0213.0413.0193.04.0368269700.0NEAR BAY
6-122.2537.8452.02535.0489.01094.0514.03.6591299200.0NEAR BAY
7-122.2537.8452.03104.0687.01157.0647.03.1200241400.0NEAR BAY
8-122.2637.8442.02555.0665.01206.0595.02.0804226700.0NEAR BAY
9-122.2537.8452.03549.0707.01551.0714.03.6912261100.0NEAR BAY
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
18887-121.3239.2911.02640.0505.01257.0445.03.5673112000.0INLAND
18888-121.4039.3315.02655.0493.01200.0432.03.5179107200.0INLAND
18889-121.4539.2615.02319.0416.01047.0385.03.1250115600.0INLAND
18890-121.5339.1927.02080.0412.01082.0382.02.549598300.0INLAND
18891-121.5639.2728.02332.0395.01041.0344.03.7125116800.0INLAND
18892-121.0939.4825.01665.0374.0845.0330.01.560378100.0INLAND
18893-121.2139.4918.0697.0150.0356.0114.02.556877100.0INLAND
18894-121.2239.4317.02254.0485.01007.0433.01.700092300.0INLAND
18895-121.3239.4318.01860.0409.0741.0349.01.867284700.0INLAND
18896-121.2439.3716.02785.0616.01387.0530.02.388689400.0INLAND